CN109507885A - Model-free adaption AUV control method based on active disturbance rejection - Google Patents
Model-free adaption AUV control method based on active disturbance rejection Download PDFInfo
- Publication number
- CN109507885A CN109507885A CN201811561751.5A CN201811561751A CN109507885A CN 109507885 A CN109507885 A CN 109507885A CN 201811561751 A CN201811561751 A CN 201811561751A CN 109507885 A CN109507885 A CN 109507885A
- Authority
- CN
- China
- Prior art keywords
- control
- model
- auv
- disturbance rejection
- active disturbance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The model-free adaption AUV control method based on active disturbance rejection that the invention discloses a kind of, to input signal transition process arranging and its differential signal is extracted first with the differential tracker in Active Disturbance Rejection Control, then the uncertain disturbance of system is regarded as total disturbance and carries out real-time dynamic estimation and compensation to it by extended state observer, and the signal for being tracked out differential tracker is input among model-free adaptive controller, finally observe the interference effect come on the output rudder angle of model-free adaptive controller extended state observer, the final motion control for realizing AUV.The present invention overcomes the contradictions between traditional control algorithm rapidity and overshoot, substantially increase the anti-interference ability of system, and realize that simple, calculation amount is small, strong robustness, it is obvious for unknown nonlinear time-varying system control effect, it can be widely applied in the motion control of AUV, and there is good control effect.
Description
Technical field
The invention belongs to underwater robot field of intelligent control, and in particular to a kind of model-free adaption based on active disturbance rejection
AUV control method.
Background technique
Autonomous type underwater robot (AUV) is a kind of included energy, managed and controlled by its capacity of self-government itself with
The submarine navigation device for completing preplanned mission can be used for Marine Sciences investigation, harbour security monitor, underwater search and rescue, naval's application section
The fields such as administration.Movement control technology is one of key technology of underwater robot, and good movement control technology is underwater
The premise and guarantee of people's completion particular task.With the expansion of underwater robot application range, to its independence, motion control
The requirement of precision and stability is also stepped up therewith, therefore the control performance for how improving underwater robot is studied now
One important topic.
The AUV movement control technology mainly used at present has: PID control, H ∞ control, fuzzy control, ANN Control
Deng, pid control algorithm is most widely used control algolithm, but there are low-response, easy overshoot, poor anti jamming capability etc. lack
Point.The design process of the controller of H ∞ control is cumbersome, higher to the skill requirement of designer；The numerous of fuzzy control obscure
The selection of variable and subordinating degree function, which needs, carrys out design by the preferable expertise knowledge of the effect of practical proof,
It newly-designed at all inexperienced can use for a kind of；And the adaptive process of neural network needs the time, especially
When being motion amplitude and the close period when the amplitude of external interference and period and underwater robot, the study of neural network just goes out
Existing hysteresis, vibrates control.In view of the above-mentioned problems, to the MFA control aspect and Qiang Kanggan of AUV
The research for disturbing aspect of performance becomes the pith of AUV motion control research.
Summary of the invention
The present invention is directed to complicated marine environment, to make up that traditional algorithm modelling is complicated and poor anti jamming capability lacks
Point proposes a kind of model-free adaption AUV control method based on active disturbance rejection, and not needing to establish mathematical models can be realized
Intelligent motion control.
The present invention, which is that the following technical solution is employed, to be realized: the model-free adaption AUV control method based on active disturbance rejection,
The following steps are included:
(1) by the differential tracker of Active Disturbance Rejection Control to desired course, the expectation pitching letter in submarine navigation device system
It number is tracked, and extracts its differential signal；
(2) by the extended state observer of Active Disturbance Rejection Control, the uncertain disturbance of submarine navigation device system is carried out real
When dynamic estimation and compensation；
(3) it is directed to the attitude motion of submarine navigation device, establishes and is based on tight format dynamical linearization model, determine in the model
Pseudo- gradient vector form；
(4) for the pseudo- gradient vector in tight format dynamic linear models, design parameter ART network equation, to pseudo- ladder
Degree vector is estimated；
(5) according to dynamical linearization model foundation error rule function, nothing is designed by minimizing error rule function
Model self-adapted control device；
(6) signal for being tracked out the differential tracker of Active Disturbance Rejection Control, is input in model-free adaptive controller,
And obtain output rudder angle；
(7) interference for estimating Active Disturbance Rejection Control extended state observer, acts on model-free adaptive controller
Output rudder angle on, interference is compensated；
(8) motion control parameter, course, depth data in collection AUV are adjusted, and is analyzed, and then realize to AUV
Motion control.
Further, in the step (1), the algorithm design of differential tracker is as follows:
Wherein:
D=r0H, d0=hd, y=x1+hx2,Fhan is optimal synthesis function, and v (k) is system
Input signal, in Heading control, input signal is desired course, in deep-controlled, input signal be desired pitching, x1
(k) the tracking signal for being v (k), x2It (k) is x1(k) differential signal, r0 is velocity factor, directly proportional to tracking velocity, and h is filter
The wave factor, primarily directed to noise, the value of filtering factor is bigger, is just more obvious to the filter effect of noise, a, d, y, a0、d0For
Intermediate variable.
Further, in the step (2), the control algorithm design of the extended state observer is as follows:
Wherein:For system output quantity, in Heading control, system output quantity is the current course of AUV, deep-controlled
In, system output quantity is the current pitching of AUV,For by extended state observerEstimation,For by expanding shape
The system of state observerDifferential estimation,ForDifferential,ForDifferential,Ocean current to estimate disturbs,ForDifferential；l1、l2、l3、b0For kinematic parameter, δ is that controller exports rudder angle.
Further, in the step (3), the pseudo- gradient vector form of the tight format dynamical linearization model are as follows:
Y (k+1)=y (k)+φ (k) Δ u (k)
Wherein: y (k) is system output quantity, and y (k+1) is the system output quantity of subsequent time, in Heading control, system
Output quantity is current course, and in deep-controlled, the output quantity of system is current pitching, and φ (k) is pseudo- partial derivative, Δ u (k)=
U (k)-u (k-1), wherein u (k) is the output rudder angle of controller.
Further, in the step (4), the ART network equation are as follows:
IfSo
Wherein,For the estimated value of pseudo- partial derivative,For the initial value of pseudo- partial derivative, ε is a very small just whole
Number, η is step-size factor, μ is weight coefficient, Δ y (k)=y (k)-y (k-1).
Further, in the step (5), the algorithm design of controller is as follows:
Wherein: y*(k+1) input quantity it is expected for subsequent time, in Heading control, it is expected that input quantity is desired course,
In deep-controlled, it is expected that input quantity is desired pitching, λ is weight coefficient, ρ is step-size factor.
Further, the control algorithm design of the step (6) is as follows:
Wherein:For the input signal that differential tracker is tracked out, in Heading control,For
The desired course that differential tracker is tracked out, in deep-controlled,The expectation being tracked out for differential tracker
Pitching.
Further, the control algorithm design in the step (7) is as follows:
Wherein: The interference estimated for extended state observer.
Compared with prior art, the advantages and positive effects of the present invention are:
The present invention program combines Active Disturbance Rejection Control with MFA control, and the differential of Active Disturbance Rejection Control is tracked
The interference that the signal and extended state observer that device is tracked out estimate is added in the control amount of MFA control
On, differential tracker gives input signal transition process arranging, can provide a stable input signal, effectively overcome traditional calculation
Contradiction between method rapidity and overshoot, the presence of extended mode observer can achieve real-time dynamic estimation and compensating disturbance
Function, to substantially increase the anti-interference ability of system；The presence of model-free adaption part not needing to establish to appoint
What mathematical model, and realize simple, calculation amount is small, strong robustness；In addition, having for unknown nonlinear time-varying system bright
Aobvious control effect.This programme compensates for the disadvantage that traditional algorithm modelling is complicated and anti-interference ability is weak, greatly promotes
AUV motion control effects under complex environment guarantee that AUV more smoothly completes task.
Detailed description of the invention
Fig. 1 is submarine navigation device course control system schematic diagram；
Fig. 2 is submarine navigation device depth control system schematic diagram；
Fig. 3 is the Heading control schematic diagram of the model-free adaption based on active disturbance rejection；
Fig. 4 is the deep-controlled schematic diagram of two close cycles of the model-free adaption based on active disturbance rejection；
Fig. 5 for the model-free adaption based on active disturbance rejection in the case of no ocean current interference Heading control analogous diagram；
Fig. 6 is the Heading control analogous diagram of the model-free adaption in the case of having ocean current interference based on active disturbance rejection；
Fig. 7 for the model-free adaption based on active disturbance rejection in the case of no ocean current interference orientation tracking analogous diagram；
Fig. 8 is the orientation tracking analogous diagram of the model-free adaption in the case of having ocean current interference based on active disturbance rejection；
Fig. 9 for the model-free adaption based on active disturbance rejection in the case of no ocean current interference pitch control analogous diagram；
Figure 10 is the pitch control analogous diagram of the model-free adaption in the case of having ocean current interference based on active disturbance rejection；
Figure 11 for the model-free adaption based on active disturbance rejection in the case of no ocean current interference following in elevation analogous diagram；
Figure 12 is the following in elevation analogous diagram of the model-free adaption in the case of having ocean current interference based on active disturbance rejection.
Specific embodiment
The present invention discloses a kind of AUV control method of model-free adaption based on active disturbance rejection, first with active disturbance rejection control
Differential tracker in system to input signal transition process arranging and extracts its differential signal, and then extended state observer is system
The uncertain disturbance of system regards total disturbance as and carries out real-time dynamic estimation and compensation to it, and differential tracker is tracked out
Signal be input among model-free adaptive controller, finally by extended state observer observe come interference effect in nothing
On the output rudder angle of model self-adapted control device, the final motion control for realizing AUV.
Its reasonable combination for mainly realizing Active Disturbance Rejection Control and MFA control, by the micro- of Active Disturbance Rejection Control
The signal for dividing tracker to be tracked out is input among model-free adaptive controller, and extended state observer is estimated
Interference is added on the output control amount of model-free adaptive controller.Wherein, to overcome traditional algorithm rapidity and overshoot
Between contradiction, design and introduce the differential tracker of active disturbance rejection, can track reference input signal and obtain its differential letter
Number, AUV direction controller reference-input signal is desired course, and deep-controlled middle reference-input signal is desired pitching；It is most
The anti-interference ability that system may be improved designs and introduces extended state observer, all of underwater robot system
Uncertain disturbance regards total disturbance as and carries out real-time dynamic estimation and compensation to it, and emphasis considers ocean current to the interference shadow of AUV
It rings；In addition, in order to overcome the problems, such as to be difficult to set up mathematical models, invention introduces MFA control part,
It is only necessary to know that the inputoutput data of system, and realize that simple, calculation amount is small, strong robustness, when for unknown nonlinear
Change system control effect is also fairly obvious, meanwhile, the interference of Unmarried pregnancy is not present in MFA control, therefore opposite
Traditional algorithm, MFA control can have good control when the signals such as desired course, expectation pitching mutate
Effect processed.
Scheme proposed by the invention can be widely applied to underwater robot Heading control, it is deep-controlled in, control
Principle is as shown in Figs 1-4, in order to which the above objects, features and advantages of the present invention is more clearly understood, below with underwater
For the Heading control of robot, and the present invention will be further described in conjunction with attached drawing:
Underwater robot Heading control:
As shown in Figure 1, be submarine navigation device course control system schematic diagram, according to system input desired course and
The current course of sensor detection calculates desired rudder angle by control algolithm, changes the reality of AUV by changing rudder angle value
Course traces into the actual heading of AUV on desired course as far as possible, and good course control system requires AUV can be quick
Tracking desired course and overshoot it is smaller, the ability for resisting environmental disturbances is strong, the present embodiment be directed to AUV Heading control system
System, using the MFA control algorithm based on active disturbance rejection, environmental disturbances use ocean current interference, specifically as shown in figure 3, being
The flow diagram of MFA control based on active disturbance rejection.
In ocean current interference, the desired course of AUV is provided first, and the current of AUV is obtained by AHRS sensor
Course, the desired course and its differential signal that the desired course of AUV is tracked out by the differential tracker of active disturbance rejection,
Using the desired course being tracked out as the input signal of MFA control.Meanwhile the practical boat for detecting AHRS
Estimated value and its differential are obtained to by extended state observer, and estimates the interference of system, the interference estimated is made
With the final output rudder angle of AUV on the output rudder angle of MFA control, is obtained, by the output rudder angle for changing AUV
Change the actual heading of AUV, specific:
Step 1: by the differential tracker of Active Disturbance Rejection Control to the desired course signal arrangement in submarine navigation device system
Transient process simultaneously extracts its differential signal, and the algorithm design of differential tracker is as follows:
Wherein:
D=rh, d0=hd,
In formula: ψdIt (k) is the desired course of last moment,For the current desired course being tracked out,Desired course for the last moment being tracked out,ForDifferential,For
Differential, fhan be optimal synthesis function, r0 is velocity factor, and directly proportional to tracking velocity, h is filtering factor, mainly needle
To noise, the value of filtering factor is bigger, is just more obvious to the filter effect of noise, a, d, y, a0、d0For intermediate variable, this implementation
R is taken in example0=0.01, h=2000.
Step 2: being carried out to all uncertain disturbances of system real-time by the extended state observer of Active Disturbance Rejection Control
The control algorithm design of dynamic estimation and compensation, extended state observer is as follows:
Wherein: ψ is current course,For the current course estimated,For the differential in the current course estimated,ForDifferential,ForDifferential；For the interference estimated,ForDifferential, δ be vertical rudder angle, l1、l2、l3、
b0For controller parameter, l is taken in the present embodiment1=2.9, l2=1.9, l3=0.02, b0=0.02.
Step 3: being directed to the attitude motion of submarine navigation device, establishes and be based on tight format dynamic linear models, determine the model
In pseudo- gradient vector form:
ψ (k+1)=ψ (k)+φ (k) Δ u (k)
Wherein: ψ (k) is desired course, and ψ (k+1) is the desired course of subsequent time, and φ (k) is pseudo- partial derivative, Δ u (k)
=u (k)-u (k-1), wherein u (k) is desired vertical rudder angle.
Step 4: for the pseudo- gradient vector in tight format dynamic linear models, design parameter ART network equation is right
Pseudo- gradient vector is estimated:
IfSo
Wherein,For the estimated value of pseudo- partial derivative,For the initial value of pseudo- partial derivative, ε is a very small just whole
Number, η is step-size factor, μ is weight coefficient, takes η=0.0001 in the present embodiment, μ=0.01, ε=0.00001,
Step 5: according to dynamical linearization model foundation error rule function, by minimizing error rule function design
Controller out:
Wherein: ψ*It (k+1) is the desired course of subsequent time, λ is weight coefficient, ρ is step-size factor, is taken in the present embodiment
λ=0.01, ρ=70.
Step 6: the signal that the differential tracker of Active Disturbance Rejection Control is tracked out is input to model-free adaptive controller
In, control algolithm is as follows:
Wherein:The desired course being tracked out for differential tracker.
Step 7: the interference that Active Disturbance Rejection Control extended state observer is estimated, acts on model-free adaption control
On the output rudder angle of system, interference is compensated, control algolithm is as follows:
Wherein: The interference estimated for extended state observer.
Step 8: adjustment control parameter r0、h、l1、l2、l3、b0、η、μ、λ、ρ、And analysis and Control curve.
Simulation result is shown in Fig. 5-Figure 12, Fig. 5 for the model-free adaption based on active disturbance rejection in the case of no ocean current interference boat
It to control analogous diagram, and compares, emulates with Active Disturbance Rejection Control, MFA control and traditional PID control
The result shows that for Active Disturbance Rejection Control compared to traditional PID control, the rise time is long in the case where not interfering with, it cannot be good
Meet demand for control, MFA control and the MFA control algorithm rise time based on active disturbance rejection compare
PID and active disturbance rejection time are short, wherein the MFA control rise time based on active disturbance rejection is most short, is most able to satisfy control and wants
It asks, in addition in the case where not interfering with, four kinds of equal non-overshoots of control method.Fig. 6 is in the case of ocean current interference is added based on certainly
The Heading control analogous diagram of the model-free adaption of anti-interference, and with Active Disturbance Rejection Control, MFA control and tradition
PID control compare, simulation result shows there are ocean current interference, and biggish overshoot occurs in PID control,
Be not well positioned to meet demand for control, Active Disturbance Rejection Control illustrates its strong antijamming capability still without overshoot, but the rise time according to
It is so very long, demand for control can not be met well, the MFA control rise time is short, lesser overshoot, base occurs
It is identical as model-free adaption in the MFA control rise time of active disturbance rejection, but overshoot reduces, it is smaller super sacrificing
The rise time is greatly shortened in the case of tune, it is believed that realize preferable control effect.It is compared by Fig. 5 and Fig. 6 it is found that based on certainly
The MFA control of anti-interference combines the advantages of Active Disturbance Rejection Control and MFA control, realizes simple, calculating
Measure small, and strong antijamming capability.Fig. 7 and Fig. 8 is the orientation tracking curve under noiseless and noisy condition, is based on active disturbance rejection
MFA control can be very good tracking course variation, the rise time is most short and overshoot is smaller.
Fig. 9-12 is pitch control analogous diagram, by comparison as can be seen that the MFA control based on active disturbance rejection
Preferable control effect is also shown in pitch control.
The above described is only a preferred embodiment of the present invention, being not that the invention has other forms of limitations, appoint
What those skilled in the art changed or be modified as possibly also with the technology contents of the disclosure above equivalent variations etc.
It imitates embodiment and is applied to other fields, but without departing from the technical solutions of the present invention, according to the technical essence of the invention
Any simple modification, equivalent variations and remodeling to the above embodiments, still fall within the protection scope of technical solution of the present invention.
Claims (8)
1. the model-free adaption AUV control method based on active disturbance rejection, which comprises the following steps:
(1) by the differential tracker of Active Disturbance Rejection Control in submarine navigation device system desired course, expectation Pitch signal into
Line trace, and extract its differential signal；
(2) by the extended state observer of Active Disturbance Rejection Control, the uncertain disturbance of submarine navigation device system is moved in real time
State estimation and compensation；
(3) it is directed to the attitude motion of submarine navigation device, establishes and is based on tight format dynamical linearization model, determine the puppet in the model
Gradient vector form；
(4) for the pseudo- gradient vector in tight format dynamic linear models, design parameter ART network equation, to pseudo- gradient to
Amount is estimated；
(5) according to dynamical linearization model foundation error rule function, model-free is designed by minimizing error rule function
Adaptive controller；
(6) signal for being tracked out the differential tracker of Active Disturbance Rejection Control, is input in model-free adaptive controller, and
Rudder angle is exported out；
(7) interference for estimating Active Disturbance Rejection Control extended state observer acts on the defeated of model-free adaptive controller
Out on rudder angle, interference is compensated；
(8) motion control parameter, course, depth data in collection AUV are adjusted, and is analyzed, and then realize the fortune to AUV
Dynamic control.
2. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
In step (1), the algorithm design of differential tracker is as follows:
Wherein:
D=r0H, d0=hd, y=x1+hx2,Fhan is optimal synthesis function, and v (k) is the defeated of system
Enter signal, in Heading control, input signal is desired course, and in deep-controlled, input signal is desired pitching, x1(k)
For the tracking signal of v (k), x2It (k) is x1(k) differential signal, r0 is velocity factor, directly proportional to tracking velocity, and h is filtering
The factor, primarily directed to noise, the value of filtering factor is bigger, is just more obvious to the filter effect of noise, a, d, y, a0、d0For in
Between variable.
3. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
In step (2), the control algorithm design of the extended state observer is as follows:
Wherein:For system output quantity, in Heading control, system output quantity is that the current course of AUV is in deep-controlled
Output quantity of uniting is the current pitching of AUV,For by extended state observerEstimation,To be observed by expansion state
The system of deviceDifferential estimation,ForDifferential,ForDifferential,Ocean current to estimate disturbs,For
Differential；l1、l2、l3、b0For kinematic parameter, δ is that controller exports rudder angle.
4. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
In step (3), the pseudo- gradient vector form of the tight format dynamical linearization model are as follows:
Y (k+1)=y (k)+φ (k) Δ u (k)
Wherein: y (k) is system output quantity, and y (k+1) is the system output quantity of subsequent time, in Heading control, system output
Amount is current course, and in deep-controlled, the output quantity of system is current pitching, and φ (k) is pseudo- partial derivative, Δ u (k)=u
(k)-u (k-1), wherein u (k) is the output rudder angle of controller.
5. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
In step (4), the ART network equation are as follows:
IfSoWherein,For the estimated value of pseudo- partial derivative,For the initial value of pseudo- partial derivative, ε is a very small positive integer, and η is step-size factor, μ is weight coefficient, Δ y (k)=y
(k)-y(k-1)。
6. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
In step (5), the algorithm design of controller is as follows:
Wherein: y*(k+1) input quantity it is expected for subsequent time, in Heading control, it is expected that input quantity is desired course, in depth
In control, it is expected that input quantity is desired pitching, λ is weight coefficient, ρ is step-size factor.
7. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
The control algorithm design of step (6) is as follows:
Wherein:For the input signal that differential tracker is tracked out, in Heading control,For differential
The desired course that tracker is tracked out, in deep-controlled,It bows for the expectation that differential tracker is tracked out
It faces upward.
8. the model-free adaption AUV control method according to claim 1 based on active disturbance rejection, it is characterised in that: described
Control algorithm design in step (7) is as follows:
Wherein: The interference estimated for extended state observer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811561751.5A CN109507885B (en) | 2018-12-20 | 2018-12-20 | Model-free self-adaptive AUV control method based on active disturbance rejection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811561751.5A CN109507885B (en) | 2018-12-20 | 2018-12-20 | Model-free self-adaptive AUV control method based on active disturbance rejection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109507885A true CN109507885A (en) | 2019-03-22 |
| CN109507885B CN109507885B (en) | 2020-09-25 |
Family
ID=65753882
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811561751.5A Active CN109507885B (en) | 2018-12-20 | 2018-12-20 | Model-free self-adaptive AUV control method based on active disturbance rejection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109507885B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109582026A (en) * | 2019-01-10 | 2019-04-05 | 中国海洋大学 | Autonomous Underwater Vehicle path tracking control method based on Self-tuning System sight and drift angle compensation |
| CN110221537A (en) * | 2019-07-01 | 2019-09-10 | 南京航空航天大学 | Control method, device and aero-engine limitation protection control method, device |
| CN110906933A (en) * | 2019-11-06 | 2020-03-24 | 中国海洋大学 | AUV (autonomous Underwater vehicle) auxiliary navigation method based on deep neural network |
| CN111176306A (en) * | 2020-01-24 | 2020-05-19 | 西北工业大学 | Gain-variable active-disturbance-rejection longitudinal control method for underwater vehicle |
| CN111240347A (en) * | 2020-01-17 | 2020-06-05 | 中南大学 | Unmanned aerial vehicle course angle error compensation method and system based on active disturbance rejection control |
| CN112000007A (en) * | 2020-07-17 | 2020-11-27 | 无锡卡尔曼导航技术有限公司 | Disturbance suppression control method for unmanned agricultural machinery |
| CN112034707A (en) * | 2020-08-25 | 2020-12-04 | 大连理工大学 | Improved method for model-free adaptive control |
| CN112363393A (en) * | 2020-10-27 | 2021-02-12 | 华中科技大学 | Model-free self-adaptive preset performance control method for unmanned ship power positioning |
| CN113010963A (en) * | 2021-03-04 | 2021-06-22 | 山东大学 | Variable-quality underwater vehicle obstacle avoidance method and system based on deep reinforcement learning |
| CN113983717A (en) * | 2021-11-25 | 2022-01-28 | 天津电子信息职业技术学院 | Absorption type refrigeration energy-saving system based on improved model-free adaptive control strategy |
| CN114047703A (en) * | 2021-12-01 | 2022-02-15 | 吉林大学 | Model-free self-adaptive control method of piezoelectric ceramic micro-positioning platform |
| CN114442488A (en) * | 2022-01-28 | 2022-05-06 | 青岛科技大学 | Model-free adaptive control technology based on extended state observer |
| CN114755947A (en) * | 2022-03-18 | 2022-07-15 | 大连海事大学 | No model anti-interference controller structure |
| CN114924576A (en) * | 2022-04-20 | 2022-08-19 | 中国科学院沈阳自动化研究所 | Depth setting control method suitable for underwater robot |
| CN114993591A (en) * | 2022-04-15 | 2022-09-02 | 中南大学 | LADRC-based seismic simulation vibrating table control method and system |
| CN115303455A (en) * | 2022-09-16 | 2022-11-08 | 北京大学 | Underwater bionic robot motion control method, device, equipment and storage medium |
| CN115357036A (en) * | 2022-09-09 | 2022-11-18 | 华中科技大学 | Double-layer anti-interference depth tracking control method and system of under-actuated underwater vehicle |
| CN115562312A (en) * | 2022-11-17 | 2023-01-03 | 三亚哈尔滨工程大学南海创新发展基地 | Strong-tracking full-format model-free self-adaptive ship heading control method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002023807A (en) * | 2000-06-19 | 2002-01-25 | Kyosei Kan | Method for realizing feedback control for optimally and automatically removing disturbance and device for the same |
| CN101833338A (en) * | 2010-05-17 | 2010-09-15 | 哈尔滨工程大学 | Autonomous underwater vehicle vertical plane under-actuated motion control method |
| CN103412567A (en) * | 2013-04-15 | 2013-11-27 | 上海大学 | Underwater robot depth control device based on linear active disturbance rejection technology and method thereof |
| CN106708064A (en) * | 2015-11-13 | 2017-05-24 | 中国科学院沈阳自动化研究所 | Vertical plane control method for underwater robot |
| CN107942688A (en) * | 2018-01-05 | 2018-04-20 | 哈尔滨工程大学 | Aircraft forgetting factor formula model-free adaption course heading control method in water |
| CN108196445A (en) * | 2017-12-11 | 2018-06-22 | 西北工业大学 | A kind of underwater robot posture and position control method based on two close cycles active disturbance rejection |
| US20180260008A1 (en) * | 2017-03-13 | 2018-09-13 | Samsung Electronics Co., Ltd. | Active disturbance rejection based thermal control |
-
2018
- 2018-12-20 CN CN201811561751.5A patent/CN109507885B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002023807A (en) * | 2000-06-19 | 2002-01-25 | Kyosei Kan | Method for realizing feedback control for optimally and automatically removing disturbance and device for the same |
| CN101833338A (en) * | 2010-05-17 | 2010-09-15 | 哈尔滨工程大学 | Autonomous underwater vehicle vertical plane under-actuated motion control method |
| CN103412567A (en) * | 2013-04-15 | 2013-11-27 | 上海大学 | Underwater robot depth control device based on linear active disturbance rejection technology and method thereof |
| CN106708064A (en) * | 2015-11-13 | 2017-05-24 | 中国科学院沈阳自动化研究所 | Vertical plane control method for underwater robot |
| US20180260008A1 (en) * | 2017-03-13 | 2018-09-13 | Samsung Electronics Co., Ltd. | Active disturbance rejection based thermal control |
| CN108196445A (en) * | 2017-12-11 | 2018-06-22 | 西北工业大学 | A kind of underwater robot posture and position control method based on two close cycles active disturbance rejection |
| CN107942688A (en) * | 2018-01-05 | 2018-04-20 | 哈尔滨工程大学 | Aircraft forgetting factor formula model-free adaption course heading control method in water |
Non-Patent Citations (2)
| Title |
|---|
| 杨太阳: ""船舶舵鳍联合系统的无模型控制策略研究"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 陈雪: ""无模型自适应控制方法的改进设计与仿真"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109582026B (en) * | 2019-01-10 | 2020-04-07 | 中国海洋大学 | Autonomous underwater vehicle path tracking control method based on self-tuning sight line and drift angle compensation |
| CN109582026A (en) * | 2019-01-10 | 2019-04-05 | 中国海洋大学 | Autonomous Underwater Vehicle path tracking control method based on Self-tuning System sight and drift angle compensation |
| CN110221537A (en) * | 2019-07-01 | 2019-09-10 | 南京航空航天大学 | Control method, device and aero-engine limitation protection control method, device |
| CN110906933B (en) * | 2019-11-06 | 2021-10-22 | 中国海洋大学 | AUV (autonomous Underwater vehicle) auxiliary navigation method based on deep neural network |
| CN110906933A (en) * | 2019-11-06 | 2020-03-24 | 中国海洋大学 | AUV (autonomous Underwater vehicle) auxiliary navigation method based on deep neural network |
| CN111240347A (en) * | 2020-01-17 | 2020-06-05 | 中南大学 | Unmanned aerial vehicle course angle error compensation method and system based on active disturbance rejection control |
| CN111176306A (en) * | 2020-01-24 | 2020-05-19 | 西北工业大学 | Gain-variable active-disturbance-rejection longitudinal control method for underwater vehicle |
| CN111176306B (en) * | 2020-01-24 | 2022-10-14 | 西北工业大学 | Gain-variable active-disturbance-rejection longitudinal control method for underwater vehicle |
| CN112000007A (en) * | 2020-07-17 | 2020-11-27 | 无锡卡尔曼导航技术有限公司 | Disturbance suppression control method for unmanned agricultural machinery |
| CN112034707A (en) * | 2020-08-25 | 2020-12-04 | 大连理工大学 | Improved method for model-free adaptive control |
| CN112363393A (en) * | 2020-10-27 | 2021-02-12 | 华中科技大学 | Model-free self-adaptive preset performance control method for unmanned ship power positioning |
| CN113010963A (en) * | 2021-03-04 | 2021-06-22 | 山东大学 | Variable-quality underwater vehicle obstacle avoidance method and system based on deep reinforcement learning |
| CN113983717A (en) * | 2021-11-25 | 2022-01-28 | 天津电子信息职业技术学院 | Absorption type refrigeration energy-saving system based on improved model-free adaptive control strategy |
| CN114047703A (en) * | 2021-12-01 | 2022-02-15 | 吉林大学 | Model-free self-adaptive control method of piezoelectric ceramic micro-positioning platform |
| CN114047703B (en) * | 2021-12-01 | 2024-02-23 | 吉林大学 | Model-free self-adaptive control method of piezoelectric ceramic micro-positioning platform |
| CN114442488A (en) * | 2022-01-28 | 2022-05-06 | 青岛科技大学 | Model-free adaptive control technology based on extended state observer |
| CN114755947A (en) * | 2022-03-18 | 2022-07-15 | 大连海事大学 | No model anti-interference controller structure |
| CN114755947B (en) * | 2022-03-18 | 2024-05-24 | 大连海事大学 | Model-free anti-interference controller structure |
| CN114993591A (en) * | 2022-04-15 | 2022-09-02 | 中南大学 | LADRC-based seismic simulation vibrating table control method and system |
| CN114924576A (en) * | 2022-04-20 | 2022-08-19 | 中国科学院沈阳自动化研究所 | Depth setting control method suitable for underwater robot |
| CN115357036A (en) * | 2022-09-09 | 2022-11-18 | 华中科技大学 | Double-layer anti-interference depth tracking control method and system of under-actuated underwater vehicle |
| CN115303455A (en) * | 2022-09-16 | 2022-11-08 | 北京大学 | Underwater bionic robot motion control method, device, equipment and storage medium |
| CN115303455B (en) * | 2022-09-16 | 2023-04-25 | 北京大学 | Motion control method, device and equipment for underwater bionic robot and storage medium |
| CN115562312A (en) * | 2022-11-17 | 2023-01-03 | 三亚哈尔滨工程大学南海创新发展基地 | Strong-tracking full-format model-free self-adaptive ship heading control method |
| CN115562312B (en) * | 2022-11-17 | 2023-10-13 | 哈尔滨工程大学三亚南海创新发展基地 | Strong tracking full-format model-free self-adaptive ship heading control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109507885B (en) | 2020-09-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109507885A (en) | Model-free adaption AUV control method based on active disturbance rejection | |
| VanDyke et al. | Unscented Kalman filtering for spacecraft attitude state and parameter estimation | |
| Shojaei | Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque | |
| Shojaei | Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators | |
| Shojaei | Observer-based neural adaptive formation control of autonomous surface vessels with limited torque | |
| He et al. | Uncertainty and disturbance estimator-based distributed synchronization control for multiple marine surface vehicles with prescribed performance | |
| He et al. | Nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network | |
| Wang et al. | Bounded neural adaptive formation control of multiple underactuated AUVs under uncertain dynamics | |
| CN110209180A (en) | A kind of UAV navigation method for tracking target based on HuberM-Cubature Kalman filtering | |
| Wang et al. | Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter | |
| Wang et al. | Experimental and numerical study of autopilot using Extended Kalman Filter trained neural networks for surface vessels | |
| Wei et al. | Disturbance observer based control for dynamically positioned ships with ocean environmental disturbances and actuator saturation | |
| Zhang et al. | Sliding mode prediction control for 3d path following of an underactuated auv | |
| Miao et al. | DOPH∞-based path-following control for underactuated marine vehicles with multiple disturbances and constraints | |
| Zhang et al. | Adaptive output feedback control for trajectory tracking of AUV in wave disturbance condition | |
| Liu et al. | Non-linear output feedback tracking control for AUVs in shallow wave disturbance condition | |
| Zhou et al. | Nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method | |
| Zhang et al. | Observer-based adaptive tracking control of underactuated autonomous marine vehicle with uncertainty dynamic | |
| Lei et al. | Robust singular perturbation control for 3D path following of underactuated AUVs | |
| CN115951693B (en) | Robust track tracking control method for under-actuated underwater robot | |
| Xiao | Modeling and adaptive sliding mode control of the catastrophic course of a high-speed underwater vehicle | |
| CN109828454B (en) | Multi-strategy fusion AUV motion control method based on sea condition grade switching | |
| CN109752957B (en) | Guidance instruction regulator structure of unmanned ship and design method | |
| Refsnes et al. | Output feedback control of slender body underwater vehicles with current estimation | |
| Gao et al. | Compound learning based event-triggered adaptive attitude control for underwater gliders with actuator saturation and faults |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |